Current image or video monitoring usually needs to rely on manual detection and processing of operators. More and more scenes (e.g., airports, stations, shopping malls, streets among others) are covered by cameras; however, because the monitoring system cannot analyze and track features of passengers by itself, lots of manpower is needed to perform processing and monitoring. This manner needs to deploy lots of manpower to perform monitoring and administration, and with further increase of number of cameras, it is hard to perform efficient processing or respond to emergencies.
The object of intelligent monitoring is to automatically track passengers in the video scene based on image data, and perform analysis and processing to characteristics and behaviors of each passenger. Currently, the intelligent monitoring usually only relies on conventional non-depth cameras (RGB cameras). The tracking of passengers is not accurate and is limited by the action gestures of passengers in the scene due to the limit of the camera itself, therefore, the analysis based on features of passengers cannot reach the expected effect. Depth cameras (depth video cameras) have been widely applied to application scenes such as human-machine interaction currently, but there is no mature system and method to expand their application to the intelligent monitoring field currently. Particularly, existing monitoring systems cannot realize accurate analysis on features (e.g., height, weight, motion speed) of passengers or effective detection of abnormal behaviors of passengers.